A hierarchical bi-level model predictive controller is proposed in this thesis to reduce the computational complexity of controlling a large scale air traffic control problem, using a model predictive control approach. The bi-level controller developed and tested in this research project is a combination of a global, long-term, slow-rate, centralized model predictive controller and a local, short-term, fast-rate decentralized model predictive controller that aims to cooperatively guide aircraft towards their destination while avoiding forbidden areas. The bi-level controller is compared to both single-level controllers it is contrived to explore the benefits achieved by the cooperation of the individual controllers, in the context of an air traffic control application. An accurate baseline model predictive controller is used to compare the computational efficiency advantage gained when using the bi-level control structure. The bi-level controller proves to attain a superior control performance over both its contributing parts. The bilevel controller provides a more accurate control performance than the single global centralized controller and performs better in trajectory optimization than the local decentralized controller. Furthermore, the controller performance of the accurate baseline controller can be approached with the bi-level controller at a reduced computation time.